Quantitative View Assessment (QUVIAS) method for window visibility analysis utilizing BIM, GIS and Web environments
Abstract
The developers of the construction project assess the economic feasibility of the project at the early stages of project development and analyse possible alternative solutions. This research focuses on the assessment of property attractiveness and building location problems at an early stage of project development and proposes the original method for visibility analysis based on the utilization of Building Information Modelling (BIM), Geographic Information System (GIS) and Web environments. The proposed Quantitative View Assessment (QUVIAS) method allows to assess the view mathematically and presents it as a quantitative parameter. The proposed method considers the mathematical shape of the view as a sphere and utilizes spherical coordinates that remove distortions and increase the accuracy of the analysis. The presented approach determines quantitative view coefficients for alternatives of windows, premises and buildings, including their comparison. The way of determining the view proposed in the QUVIAS method can help decision-makers to make more accurate decisions during the selection of a project development strategy. The experimental analysis proved the usefulness of the proposed QUVIAS method in the assessment of the rational building location and prediction of project revenues as well as potential usefulness in the estimation of property attractiveness.
Keyword : Building Information Modelling (BIM), GIS, Web, QUVIAS, project planning, building location, visibility analysis
How to Cite
Shkundalov, D., & Vilutienė, T. (2022). Quantitative View Assessment (QUVIAS) method for window visibility analysis utilizing BIM, GIS and Web environments. International Journal of Strategic Property Management, 26(4), 287–304. https://doi.org/10.3846/ijspm.2022.17754
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Correll, M. (1977). Early time measurements. The Physics Teacher, 15(8), 476–479. https://doi.org/10.1119/1.2339739
Crompton, J. L., & Nicholls, S. (2019). The impact of park views on property values. Leisure Sciences, 1–13. https://doi.org/10.1080/01490400.2019.1703125
Esri ArcGIS. (2021, May 18). Property value analysis. https://www.esri.com/en-us/industries/land-administration/strategies/value-analysis
Gröger, G., Kolbe, T. H., & Czerwinski, A. (Eds.). (2006). Candidate OpenGIS® CityGML implementation specification (City geography markup language). Open Geospatial Consortium, Inc. https://portal.ogc.org/files/?artifact_id=16675
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Helbich, M., & Griffith, D. A. (2016). Spatially varying coefficient models in real estate: eigenvector spatial filtering and alternative approaches. Computers, Environment and Urban Systems, 57, 1–11. https://doi.org/10.1016/j.compenvurbsys.2015.12.002
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Ja’afar, N. S., Mohamad, J., & Ismail, S. (2021). Machine learning for property price prediction and price valuation: a systematic literature review. Planning Malaysia Journal, 19(3), 411–422. https://doi.org/10.21837/pm.v19i17.1018
Jegelavičiūtė, R. (2017). Lyginamojo metodo pataisos kriterijų įtaka nekilnojamojo turto vertei (2 leidimas). Lituka ir Ko. https://portal.issn.org/resource/ISSN/2424-3809
Jenkins, S. T., & Hilkert, J. M. (1989). Line of sight stabilization using image motion compensation. SPIE 1989 Technical Symposium on Aerospace Sensing, 1111, 1–18. https://doi.org/10.1117/12.977973
Jim, C. Y., & Chen, W. Y. (2006). Impacts of urban environmental elements on residential housing prices in Guangzhou (China). Landscape and Urban Planning, 78(4), 422–434. https://doi.org/10.1016/j.landurbplan.2005.12.003
Jim, C. Y., & Chen, W. Y. (2009). Value of scenic views: hedonic assessment of private housing in Hong Kong. Landscape and Urban Planning, 91(4), 226–234. https://doi.org/10.1016/j.landurbplan.2009.01.009
Jim, C. Y., & Chen, W. Y. (2010). External effects of neighbourhood parks and landscape elements on high-rise residential value. Land Use Policy, 27(2), 662–670. https://doi.org/10.1016/j.landusepol.2009.08.027
Jusuf, S. K., Mousseau, B., Godfroid, G., & Soh, J. H. V. (2017). Path to an integrated modelling between IFC and CityGML for neighborhood scale modelling. Urban Science, 1(3), 25. https://doi.org/10.3390/urbansci1030025
Kaklauskas, A., Zavadskas, E. K., Banaitis, A., & Šatkauskas, G. (2007). Defining the utility and market value of a real estate: a multiple criteria approach. International Journal of Strategic Property Management, 11(2), 107–120. https://doi.org/10.3846/1648715X.2007.9637564
Kara, A., Van Oosterom, P., Cagdas, V., Isıkdag, U., & Lemmen, C. (2020). 3 dimensional data research for property valuation in the context of the LADM valuation information model. Land Use Policy, 98, 104179. https://doi.org/10.1016/j.landusepol.2019.104179
Kerkovits, K. (2020). Quadrature rules to calculate distortions of map projections. Cartographic Journal, 57(3), 249–260. https://doi.org/10.1080/00087041.2020.1714278
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